Robust traffic merging strategies for sensor-enabled cars using time geography
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A fast joint tracking-registration algorithm for multi-sensor systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Asynchronous multi-sensor bias estimation with sensor location uncertainty
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Nonlinear Kalman Filters and Particle Filters for integrated navigation of unmanned aerial vehicles
Robotics and Autonomous Systems
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The fusion of multiple sensory information plays a key role in cooperative driving for flexible platooning of automated vehicles over a couple of lanes within a short intervehicle distance. In this paper, the problem of online sensor fusion with spatially and temporally misaligned dissimilar sensors is considered. A spatial-temporal registration model for the popular intelligent vehicular sensors including radar, global positioning system, inertial navigation system, and camera is first developed for sensor alignment. An unscented Kalman filter (UKF) is proposed here to fuse and register these sensors that are installed on a platoon of vehicles simultaneously. When the road geometry information is available from a digital map database, a constrained UKF is further developed to improve the fusion accuracy. The effect of spatial-temporal sensor misalignment upon the vehicle-state estimation is also analyzed theoretically. Simulations show that the proposed UKF method not only can align the dissimilar vehicular sensors properly with both spatial and temporal biases, but can also obtain accurate fused tracks of vehicles in a platoon.